A key contemporary trend emerging in big data science is the Quantified Self (QS) - individuals engaged in the deliberate self-tracking of any kind of biological, physical, behavioral, or transactional information as n=1 individuals or in groups. This is giving rise to interesting pools of individual data, group data, and big data which can be interlinked to create a new era of highly-targeted value-specific consumer applications. There are significant opportunities in big data to develop models to support QS data collection, integration, analysis, and use for personal lifestyle and consumption management. There are also opportunities to provide leadership in designing consumer-friendly standards and etiquette regarding the use of personal and collective data. Next-generation QS big data applications and services could include tools for rendering QS data meaningful in behavior change, establishing baselines and variability in objective metrics, applying new kinds of pattern recognition techniques, and aggregating multiple self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. Potential limitations regarding QS activity need to be considered including consumer non-adoption, data privacy and sharing concerns, the digital divide, ease-of-use, and social acceptance.
Quantified Self Ideology: Personal Data becomes Big DataMelanie Swan
A key contemporary trend emerging in big data science is the quantified self: individuals engaged in the deliberate self-tracking of any kind of biological, physical, behavioral, or transactional information, as n=1 individuals or in groups. The quantified self is one dimension of the bigger trend to integrate and apply a variety of personal information streams including big health data (genome, transcriptome, environmentome, diseasome), quantified self data streams (biosensor, fitness, sleep, food, mood, heart rate, glucose tracking, etc.), traditional data streams (personal and family health history, prescription history) and IOT (Internet of things) activity data streams (smart home, smart car, environmental sensors, community data). This talk looks at how personal data and group data are becoming big data as individuals and communities share, collaborate, and work with large personalized data sets using novel discovery methods such as anomaly detection and exception reporting, longitudinal baseline analysis, episodic triggers, and hierarchical machine learning.
Philosophy of Big Data: Big Data, the Individual, and SocietyMelanie Swan
Philosophical concepts elucidate the impact the Big Data Era (exabytes/year of scientific, governmental, corporate, personal data being created) is having on our sense of ourselves as individuals in society as information generators in constant dialogue with the pervasive information climate.
The Philosophy of Big Data is the branch of philosophy concerned with the foundations, methods, and implications of big data; the definitions, meaning, conceptualization, knowledge possibilities, truth standards, and practices in situations involving very-large data sets that are big in volume, velocity, variety, veracity, and variability
The Internet of Things means not just that computing devices have connectivity to the cloud but that they themselves are connected to each other, and therefore that novel applications can be developed in this rich ecosystem. One area for development is linking quantified self wearable sensors with automotive sensors for applications including Fatigue Detection, Real-time Parking and Assistance, Anger/Stress Reduction, Keyless Authentication, and DIY Diagnostics.
The Future of Life Sciences 2013 for Max Planck InstituteMelanie Swan
Top 10 List of Life Sciences Opportunities - The next wave of the biotechnology revolution is underway and promises to reshape the world in ways even more transformative than the agricultural, industrial and information revolutions that preceded it. It is not nimaginable that at some point, all biological processes, human and otherwise, will be understood and managed. Some of the most likely sources of life sciences discontinuities are genomic sequencing and synthesis, synthetic biology, nanoscience and aging.
"Computing for Human Experience: Semantics empowered Cyber-Physical, Social and Ubiquitous Computing beyond the Web" Keynote at On the Move Federated Conferences, Crete, Greece, October 18, 2011.
http://www.onthemove-conferences.org/
Details: http://wiki.knoesis.org/index.php/Computi
Singularity University Live Prediction Markets Simulation & Big Data Quantita...Melanie Swan
Singularity University Live Prediction Markets Workshop & Big Data Quantitative Indicators: Creating the future means being able to understand and operate within multiple areas of rapidly changing technology. We need to know how to harness the many digital tools and indicators at our disposal. Market principles are being deployed on a larger scale in novel ways to rethink traditional institutions and allow unprecedented global-scale collaboration, communication, remuneration, and real-time information flow through prediction markets, cyber-currencies (Bitcoin, etc.), and eLabor marketplaces.
Quantified Self Ideology: Personal Data becomes Big DataMelanie Swan
A key contemporary trend emerging in big data science is the quantified self: individuals engaged in the deliberate self-tracking of any kind of biological, physical, behavioral, or transactional information, as n=1 individuals or in groups. The quantified self is one dimension of the bigger trend to integrate and apply a variety of personal information streams including big health data (genome, transcriptome, environmentome, diseasome), quantified self data streams (biosensor, fitness, sleep, food, mood, heart rate, glucose tracking, etc.), traditional data streams (personal and family health history, prescription history) and IOT (Internet of things) activity data streams (smart home, smart car, environmental sensors, community data). This talk looks at how personal data and group data are becoming big data as individuals and communities share, collaborate, and work with large personalized data sets using novel discovery methods such as anomaly detection and exception reporting, longitudinal baseline analysis, episodic triggers, and hierarchical machine learning.
Philosophy of Big Data: Big Data, the Individual, and SocietyMelanie Swan
Philosophical concepts elucidate the impact the Big Data Era (exabytes/year of scientific, governmental, corporate, personal data being created) is having on our sense of ourselves as individuals in society as information generators in constant dialogue with the pervasive information climate.
The Philosophy of Big Data is the branch of philosophy concerned with the foundations, methods, and implications of big data; the definitions, meaning, conceptualization, knowledge possibilities, truth standards, and practices in situations involving very-large data sets that are big in volume, velocity, variety, veracity, and variability
The Internet of Things means not just that computing devices have connectivity to the cloud but that they themselves are connected to each other, and therefore that novel applications can be developed in this rich ecosystem. One area for development is linking quantified self wearable sensors with automotive sensors for applications including Fatigue Detection, Real-time Parking and Assistance, Anger/Stress Reduction, Keyless Authentication, and DIY Diagnostics.
The Future of Life Sciences 2013 for Max Planck InstituteMelanie Swan
Top 10 List of Life Sciences Opportunities - The next wave of the biotechnology revolution is underway and promises to reshape the world in ways even more transformative than the agricultural, industrial and information revolutions that preceded it. It is not nimaginable that at some point, all biological processes, human and otherwise, will be understood and managed. Some of the most likely sources of life sciences discontinuities are genomic sequencing and synthesis, synthetic biology, nanoscience and aging.
"Computing for Human Experience: Semantics empowered Cyber-Physical, Social and Ubiquitous Computing beyond the Web" Keynote at On the Move Federated Conferences, Crete, Greece, October 18, 2011.
http://www.onthemove-conferences.org/
Details: http://wiki.knoesis.org/index.php/Computi
Singularity University Live Prediction Markets Simulation & Big Data Quantita...Melanie Swan
Singularity University Live Prediction Markets Workshop & Big Data Quantitative Indicators: Creating the future means being able to understand and operate within multiple areas of rapidly changing technology. We need to know how to harness the many digital tools and indicators at our disposal. Market principles are being deployed on a larger scale in novel ways to rethink traditional institutions and allow unprecedented global-scale collaboration, communication, remuneration, and real-time information flow through prediction markets, cyber-currencies (Bitcoin, etc.), and eLabor marketplaces.
Smart Data for you and me: Personalized and Actionable Physical Cyber Social ...Amit Sheth
Featured Keynote at Worldcomp'14, July 2014: http://www.world-academy-of-science.org/worldcomp14/ws/keynotes/keynote_sheth
Video of the talk at: http://youtu.be/2991W7OBLqU
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is human health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information, etc.). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will forward the concept of Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If I am an asthma patient, for all the data relevant to me with the four V-challenges, what I care about is simply, “How is my current health, and what is the risk of having an asthma attack in my personal situation, especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city. I will present examples from a couple of these.
Smart Data - How you and I will exploit Big Data for personalized digital hea...Amit Sheth
Amit Sheth's keynote at IEEE BigData 2014, Oct 29, 2014.
Abstract from:
http://cci.drexel.edu/bigdata/bigdata2014/keynotespeech.htm
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is personalized digital health that related to taking better decisions about our health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (e.g., information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, “How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city.
TRANSFORMING BIG DATA INTO SMART DATA: Deriving Value via Harnessing Volume, ...Amit Sheth
Keynote given at ICDE2014, April 2014. Details at: http://ieee-icde2014.eecs.northwestern.edu/keynotes.html
A video of a version of this talk is available here: http://youtu.be/8RhpFlfpJ-A
(download to see many hidden slides).
Two versions of this talk, targeted at Smart Energy and Personalized Digital Health domains/apps at: http://wiki.knoesis.org/index.php/Smart_Data
Previous (older) version replaced by this version: http://www.slideshare.net/apsheth/big-data-to-smart-data-keynote
Presented at the Panel on
Sensor, Data, Analytics and Integration in Advanced Manufacturing, at the Connected Manufacturing track of Bosch-USA organized "Leveraging Public-Private Partnerships for Regional Growth Summit". Panel statement: Sensors, data and analytics are the core of any smart manufacturing system. What are the main challenges to create actionable outputs, replicate systems and scale efficiency gains across industries?
Moderator: Thomas Stiedl, Bosch
Panelists:
1. Amit Sheth, Wright State University
2. Howie Choset, Carnegie Melon University
3. Nagi Gebraeel, Georgia Institute of Technology
4. Brian Anthony, Massachusetts Institute of Technology
5. Yarom Polosky, Oak Ridget National Laboratory
For in-depth look:
Smart IoT: IoT as a human agent, human extension, and human complement
http://amitsheth.blogspot.com/2015/03/smart-iot-iot-as-human-agent-human.html
Semantic Gateway: http://knoesis.org/library/resource.php?id=2154
SSN Ontology: http://knoesis.org/library/resource.php?id=1659
Applications of Multimodal Physical (IoT), Cyber and Social Data for Reliable and Actionable Insights: http://knoesis.org/library/resource.php?id=2018
Smart Data: Transforming Big Data into Smart Data...: http://wiki.knoesis.org/index.php/Smart_Data
Historic use of the term Smart Data (2004): http://www.scribd.com/doc/186588820
Presentation at the AAAI 2013 Fall Symposium on Semantics for Big Data, Arlington, Virginia, November 15-17, 2013
Additional related material at: http://wiki.knoesis.org/index.php/Smart_Data
Related paper at: http://www.knoesis.org/library/resource.php?id=1903
Abstract: We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. We organize our research around the five V's of Big Data, where four of the Vs are harnessed to produce the fifth V - value. To handle the challenge of Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle the challenge of Variety, we resort to the use of semantic models and annotations of data so that much of the intelligent processing can be done at a level independent of heterogeneity of data formats and media. To handle the challenge of Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize new concepts, entities and facts. To handle Veracity, we explore the formalization of trust models and approaches to glean trustworthiness. The above four Vs of Big Data are harnessed by the semantics-empowered analytics to derive Value for supporting practical applications transcending physical-cyber-social continuum.
Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact...Matthew Lease
Presented at the 31st ACM User Interface Software and Technology Symposium (UIST), 2018. Paper: https://www.ischool.utexas.edu/~ml/papers/nguyen-uist18.pdf
Talk given at Delft University speaker series on "Crowd Computing & Human-Centered AI" (https://www.academicfringe.org/). November 23, 2020. Covers two 2020 works:
(1) Anubrata Das, Brandon Dang, and Matthew Lease. Fast, Accurate, and Healthier: Interactive Blurring Helps Moderators Reduce Exposure to Harmful Content. In Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2020.
Alexander Braylan and Matthew Lease. Modeling and Aggregation of Complex Annotations via Annotation Distances. In Proceedings of the Web Conference, pages 1807--1818, 2020.
Ehealth: enabling self-management, public health 2.0 and citizen scienceKathleen Gray
Invited presentation, Technology in Diabetes Joint Symposium, Australian Diabetes Society & Australian Diabetes Educators Association Annual Scientific Meeting, August 2014.
Web Observatories, e-Research and the Importance of Collaboration. WST 2014 Webinar series, 20th March 2014
See Web Science Trust http://webscience.org/
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).
Presentation by Prof. Fernando MArtin-Sanchez, Director of the Health and Biomedical Informatics Centre (HaBIC) of the University of Melbourne at at the Panel on Big Data in Health and Biomedical Research, at the annual AMIA 2013 Conference, 19th November, Washington DC
Smart Data for you and me: Personalized and Actionable Physical Cyber Social ...Amit Sheth
Featured Keynote at Worldcomp'14, July 2014: http://www.world-academy-of-science.org/worldcomp14/ws/keynotes/keynote_sheth
Video of the talk at: http://youtu.be/2991W7OBLqU
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is human health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information, etc.). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will forward the concept of Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If I am an asthma patient, for all the data relevant to me with the four V-challenges, what I care about is simply, “How is my current health, and what is the risk of having an asthma attack in my personal situation, especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city. I will present examples from a couple of these.
Smart Data - How you and I will exploit Big Data for personalized digital hea...Amit Sheth
Amit Sheth's keynote at IEEE BigData 2014, Oct 29, 2014.
Abstract from:
http://cci.drexel.edu/bigdata/bigdata2014/keynotespeech.htm
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is personalized digital health that related to taking better decisions about our health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (e.g., information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, “How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city.
TRANSFORMING BIG DATA INTO SMART DATA: Deriving Value via Harnessing Volume, ...Amit Sheth
Keynote given at ICDE2014, April 2014. Details at: http://ieee-icde2014.eecs.northwestern.edu/keynotes.html
A video of a version of this talk is available here: http://youtu.be/8RhpFlfpJ-A
(download to see many hidden slides).
Two versions of this talk, targeted at Smart Energy and Personalized Digital Health domains/apps at: http://wiki.knoesis.org/index.php/Smart_Data
Previous (older) version replaced by this version: http://www.slideshare.net/apsheth/big-data-to-smart-data-keynote
Presented at the Panel on
Sensor, Data, Analytics and Integration in Advanced Manufacturing, at the Connected Manufacturing track of Bosch-USA organized "Leveraging Public-Private Partnerships for Regional Growth Summit". Panel statement: Sensors, data and analytics are the core of any smart manufacturing system. What are the main challenges to create actionable outputs, replicate systems and scale efficiency gains across industries?
Moderator: Thomas Stiedl, Bosch
Panelists:
1. Amit Sheth, Wright State University
2. Howie Choset, Carnegie Melon University
3. Nagi Gebraeel, Georgia Institute of Technology
4. Brian Anthony, Massachusetts Institute of Technology
5. Yarom Polosky, Oak Ridget National Laboratory
For in-depth look:
Smart IoT: IoT as a human agent, human extension, and human complement
http://amitsheth.blogspot.com/2015/03/smart-iot-iot-as-human-agent-human.html
Semantic Gateway: http://knoesis.org/library/resource.php?id=2154
SSN Ontology: http://knoesis.org/library/resource.php?id=1659
Applications of Multimodal Physical (IoT), Cyber and Social Data for Reliable and Actionable Insights: http://knoesis.org/library/resource.php?id=2018
Smart Data: Transforming Big Data into Smart Data...: http://wiki.knoesis.org/index.php/Smart_Data
Historic use of the term Smart Data (2004): http://www.scribd.com/doc/186588820
Presentation at the AAAI 2013 Fall Symposium on Semantics for Big Data, Arlington, Virginia, November 15-17, 2013
Additional related material at: http://wiki.knoesis.org/index.php/Smart_Data
Related paper at: http://www.knoesis.org/library/resource.php?id=1903
Abstract: We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. We organize our research around the five V's of Big Data, where four of the Vs are harnessed to produce the fifth V - value. To handle the challenge of Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle the challenge of Variety, we resort to the use of semantic models and annotations of data so that much of the intelligent processing can be done at a level independent of heterogeneity of data formats and media. To handle the challenge of Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize new concepts, entities and facts. To handle Veracity, we explore the formalization of trust models and approaches to glean trustworthiness. The above four Vs of Big Data are harnessed by the semantics-empowered analytics to derive Value for supporting practical applications transcending physical-cyber-social continuum.
Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact...Matthew Lease
Presented at the 31st ACM User Interface Software and Technology Symposium (UIST), 2018. Paper: https://www.ischool.utexas.edu/~ml/papers/nguyen-uist18.pdf
Talk given at Delft University speaker series on "Crowd Computing & Human-Centered AI" (https://www.academicfringe.org/). November 23, 2020. Covers two 2020 works:
(1) Anubrata Das, Brandon Dang, and Matthew Lease. Fast, Accurate, and Healthier: Interactive Blurring Helps Moderators Reduce Exposure to Harmful Content. In Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2020.
Alexander Braylan and Matthew Lease. Modeling and Aggregation of Complex Annotations via Annotation Distances. In Proceedings of the Web Conference, pages 1807--1818, 2020.
Ehealth: enabling self-management, public health 2.0 and citizen scienceKathleen Gray
Invited presentation, Technology in Diabetes Joint Symposium, Australian Diabetes Society & Australian Diabetes Educators Association Annual Scientific Meeting, August 2014.
Web Observatories, e-Research and the Importance of Collaboration. WST 2014 Webinar series, 20th March 2014
See Web Science Trust http://webscience.org/
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).
Presentation by Prof. Fernando MArtin-Sanchez, Director of the Health and Biomedical Informatics Centre (HaBIC) of the University of Melbourne at at the Panel on Big Data in Health and Biomedical Research, at the annual AMIA 2013 Conference, 19th November, Washington DC
Quantified Self is a movement of people that track individual daily activities metrics through technology with the goal of self improvement. Quantified selfers believe that the meaning of life is improving his own talent, no matter which it is. From a business point of view Quantified Self allows companies to reach consumers while they change their shopping habits and to deal with them in a new and different way.
This report - part of the "Inspiring Route" project - analyses and understands the main themes related to Quantified Self through stories, examples, numbers, case studies.
In het weekend van 6 & 7 juli 2013 organiseerde Labs voor het Ministerie van Buitenlandse Zaken een hackathon. Yuri van Geest presenteerde zijn visie op innovatie en organisaties in tijden van overvloed en singularity. Zie veder http://hackathonbuza.nl
Doctoral Consortium: Applying Quantified Self Approaches to Support Reflectiv...veronicarp
Slides of my presentation at the MobileHCI Conference, September 2012, San Francisco. We presented and discussed our approach at the Doctoral Consortium.
The Quantified Self - Self Knowledge Through Numberscityofthedes
On Friday, June 22, 2012, Nick Tazik and I gave this presentation on the concept of the Quantified Self to members of Digital VU at Vanderbilt University.
Many thanks to Ernesto Ramirez from QuantifiedSelf.com for a few of these slides.
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
We are in an exciting new era of scientific discovery with a greatly expanded range of possibilities due to big data, computation, and crowd participation
Data Science Innovations is a guest lecture for the Advanced Data Analytics (an Introduction) course at the Advanced Analytics Institute at University of Technology Sydney
HSC Event, https://www.youtube.com/watch?v=g0FakQaUvPM
,digital health ,orcha ,digital phenotype ,dynamic consent ,real world data ,data in the wild ,ecological momentary assessment
BROWN BAG TALK WITH MICAH ALTMAN, SOURCES OF BIG DATA FOR SOCIAL SCIENCESMicah Altman
This talk, is part of the MIT Program on Information Science brown bag series (http://informatics.mit.edu)
This talk reviews emerging big data sources for social scientific analysis and explores the challenges these present. Many of these sources pose distinct challenges for acquisition, processing, analysis, inference, sharing, and preservation.
Dr Micah Altman is Director of Research and Head/Scientist, Program on Information Science for the MIT Libraries, at the Massachusetts Institute of Technology. Dr. Altman is also a Non-Resident Senior Fellow at The Brookings Institution. Prior to arriving at MIT, Dr. Altman served at Harvard University for fifteen years as the Associate Director of the Harvard-MIT Data Center, Archival Director of the Henry A. Murray Archive, and Senior Research Scientist in the Institute for Quantitative Social Sciences.
Dr. Altman conducts research in social science, information science and research methods -- focusing on the intersections of information, technology, privacy, and politics; and on the dissemination, preservation, reliability and governance of scientific knowledge.
The Pew Research Center’s Internet & American Life Project and Elon University’s Imagining the Internet Center asked digital stakeholders to weigh two scenarios for 2020, select the one most likely to evolve, and elaborate on the choice. One sketched out a relatively positive future where Big Data are drawn together in ways that will improve social, political, and economic intelligence. The other expressed the view that Big Data could cause more problems than it solves between now and 2020
Future Technological Practices: Medical Librarians’ Skills and Information Structures for Continued Effectiveness in a Changing Environment
Patricia F. Anderson, Skye Bickett, AHIP, Joanne Doucette, Pamela R. Herring, AHIP, Judith Kammerer, AHIP, Andrea Kepsel, AHIP, Tierney Lyons, Scott McLachlan, Ingrid Tonnison, and Lin Wu, AHIP
Social Media Datasets for Analysis and Modeling Drug Usageijtsrd
This paper based on the research carried out in the area of data mining depends for managing bulk amount of data with mining in social media on using composite applications for performing more sophisticated analysis. Enhancement of social media may address this need. The objective of this paper is to introduce such type of tool which used in social network to characterised Medicine Usage. This paper outlined a structured approach to analyse social media in order to capture emerging trends in medicine abuse by applying powerful methods like Machine Learning. This paper describes how to fetch important data for analysis from social network. Then big data techniques to extract useful content for analysis are discussed. Sindhu S. B | Dr. B. N Veerappa "Social Media Datasets for Analysis and Modeling Drug Usage" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25246.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/25246/social-media-datasets-for-analysis-and-modeling-drug-usage/sindhu-s-b
IQPC Enterprise IT Security Exchange, March 10, 2013
This presentation looks at the risks and rewards and security and privacy implications of Big Data Analytics.
June 2015 (142) MIS Quarterly Executive 67The Big Dat.docxcroysierkathey
June 2015 (14:2) | MIS Quarterly Executive 67
The Big Data Industry1 2
Big Data receives a lot of press and attention—and rightly so. Big Data, the combination of
greater size and complexity of data with advanced analytics,3 has been effective in improving
national security, making marketing more effective, reducing credit risk, improving medical
research and facilitating urban planning. In leveraging easily observable characteristics and
events, Big Data combines information from diverse sources in new ways to create knowledge,
make better predictions or tailor services. Governments serve their citizens better, hospitals
are safer, firms extend credit to those previously excluded from the market, law enforcers catch
more criminals and nations are safer.
Yet Big Data (also known in academic circles as “data analytics”) has also been criticized as a
breach of privacy, as potentially discriminatory, as distorting the power relationship and as just
“creepy.”4 In generating large, complex data sets and using new predictions and generalizations,
firms making use of Big Data have targeted individuals for products they did not know they
needed, ignored citizens when repairing streets, informed friends and family that someone
is pregnant or engaged, and charged consumers more based on their computer type. Table 1
summarizes examples of the beneficial and questionable uses of Big Data and illustrates the
1 Dorothy Leidner is the accepting senior editor for this article.
2 This work has been funded by National Science Foundation Grant #1311823 supporting a three-year study of privacy online. I
wish to thank the participants at the American Statistical Association annual meeting (2014), American Association of Public Opin-
ion Researchers (2014) and the Philosophy of Management conference (2014), as well as Mary Culnan, Chris Hoofnagle and Katie
Shilton for their thoughtful comments on an earlier version of this article.
3 Both the size of the data set, due to the volume, variety and velocity of the data, as well as the advanced analytics, combine to
create Big Data. Key to definitions of Big Data are that the amount of data and the software used to analyze it have changed and
combine to support new insights and new uses. See also Ohm, P. “Fourth Amendment in a World without Privacy,” Mississippi.
Law Journal (81), 2011, pp. 1309-1356; Boyd, D. and Crawford, K. “Critical Questions for Big Data: Provocations for a Cultural,
Technological, and Scholarly Phenomenon,” Information, Communication & Society (15:5), 2012, pp. 662-679; Rubinstein, I. S.
“Big Data: The End of Privacy or a New Beginning?,” International Data Privacy Law (3:2), 2012, pp. 74-87; and Hartzog, W. and
Selinger, E. “Big Data in Small Hands,” Stanford Law Review Online (66), 2013, pp. 81-87.
4 Ur, B. et al. “Smart, Useful, Scary, Creepy: Perceptions of Online Behavioral Advertising,” presented at the Symposium On
Usable Privacy and Security, July 11-13, 2 ...
Supervised Multi Attribute Gene Manipulation For Cancerpaperpublications3
Abstract: Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems.
They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery.
Big data for healthcare analytics final -v0.3 mizYusuf Brima
Sources of Big Data in Health (a comparative description of national and international data sources and identification of new/emerging sources of data)
AI WORLD: I-World: EIS Global Innovation Platform: BIG Knowledge World vs. BI...Azamat Abdoullaev
Future World Projects
Global Intelligence Platform
Smart World
Smart Nation
Smart Cities Global Initiative
Smart Superpower Projects
Big Data and Big Knowledge, etc.
AI Health Agents: Longevity as a Service in the Web3 GenAI Quantum RevolutionMelanie Swan
Health Agents are a form of Math Agent as the concept of a personalized AI health advisor delivering “healthcare by app” instead of “sickcare by appointment.” Mobile devices
can check health 1000 times per minute as opposed to the standard one time per year doctor’s office visit, and model virtual patients in the digital twin app. As any AI agent, Health Agents “speak” natural language to humans and formal language to the computational infrastructure, possibly outputting the mathematics of personalized homeostatic health as part of their operation. Health Agents could facilitate the ability of physicians to oversee the health of thousands of individuals at a time. This could ease overstressed healthcare systems and contribute to physician well-being and the situation that (per the World Health Organization) more than half of the global population is still not covered by essential health services.
The computational infrastructure is becoming a vast interconnected fabric of formal methods, including per a major shift from 2d grids to 3d graphs in machine learning architectures
The implication is systems-level digital science at unprecedented scale for discovery in a diverse range of scientific disciplines
We know that we are in an AI take-off, what is new is that we are in a math take-off. A math take-off is using math as a formal language, beyond the human-facing math-as-math use case, for AI to interface with the computational infrastructure. The message of generative AI and LLMs (large language models like GPT) is not that they speak natural language to humans, but that they speak formal languages (programmatic code, mathematics, physics) to the computational infrastructure, implying the ability to create a much larger problem-solving apparatus for humanity-benefitting applications in biology, energy, and space science, however not without risk.
This work introduces “quantum intelligence” as a concept of intelligence for operating in the quantum realm may help in a potential AI-Quantum Computing convergence (~2030e), and towards the realization of SRAI for well-being (economics, health, energy, space). “Scale-free intelligence” is formulated as a generic capacity for learning.
AI did not spring onto the scene with chatGPT, but is in an ongoing multi-year adoption. A transition may be underway from an information society to a knowledge society (one tempered and specifically using knowledge to improve the human condition). AI is a dual-use technology with both significant risk and upleveling possibilities.
SRAI for well-being is a social objective, and also a technological objective. SRAI is part of AI development and within the technological trajectory of harnessing all scales of physical reality ranging from quantum materials to space exploration.
Conceptually, thinking in quantum and relativistic terms expands the physical worldview, and likewise the social worldview of entities inhabiting the larger world. Practically, SRAI may be realized in phases: short-term regulation and registries, medium-term agents learning to implement human values with internal reward functions, and long-term responsible human-AI entities acting in partnership in a future of SRAI for well-being.
The Human-AI Odyssey: Homerian Aspirations towards Non-labor IdentityMelanie Swan
The visionary progression in The Odyssey from shipbuilding to seafaring to advanced civilization informs contemporary tension in the human-AI relation forcing a broader articulation of human-identity beyond labor-identity. Edith Hall analyzes why one of the earliest known literatures, The Odyssey, remains a central cultural trope with numerous references in the storytelling vernacular of all eras, ranging from 1860s British theater to a highly-watched 1990 episode of The Simpsons. The argument is that The Odyssey provides a constant aspirational reference for human identity – who we think we are and where we are going on the epic journey of life, especially at the current crossroad in our relationship with technology.
The contemporary moment finds humanity, and the humanities, experiencing an identity crisis in the relationship with technology. Information science is having an ever more pervasive role in academia, and the machine economy continues to offload vast classes of tasks to labor-saving technology giving rise to two questions. First, at the level of labor-identity, humans wonder who they are as they have long defined their sense of self through their professional participation in the economy. Second, at the level of human-identity, with AI now performing cognitive labor in addition to physical labor, humans wonder if there is anything that remains uniquely human.
The effect of The Odyssey is to provide world-expanding imaginaries to change the way we see ourselves as subjects; in this way, Homer is an early modernist in reconfiguring our self-concept.
This work applies a philosophy (of literature)-aided information science method to discuss how Homer’s Odyssey persists as a literary imaginary to help us think through potential futures of human-AI flourishing as rapid automation continues to impact humanity. The intensity of the human-AI relation is likely to increase, which invites thought leadership to steward the transition to a potential AI abundance economy with fulfilling human-technology collaboration.
The shipbuilding-seafaring-advanced civilization progression in The Odyssey identifies that the human-AI relation is not one of the labor-identity-crisis of “robots stealing our jobs,” but rather one of the more difficult challenge of envisioning who we can be in the new larger world of human-AI partnership addressing a larger set of planetary-scale problems. Towards this new configuration of human-AI relation, the longer-term may hold radically different notions of identity, as we become physical-virtual hybrids, augmented post-disease entities in the health-faring, space-civilizing, energy-marshalling post-scarcity cultures of the future.
AdS Biology and Quantum Information ScienceMelanie Swan
Quantum Information Science is a fast-growing discipline advancing many areas of science such as cryptography, chemistry, finance, space science, and biology. In particular AdS/Biology, an interpretation of the AdS/CFT correspondence in biological systems, is showing promise in new biophysical mathematical models of topology (Chern-Simons (solvable QFT), knotting, and compaction). For example, one model of neurodegenerative disease takes a topological view of protein buildup (AB plaques and tau tangles in Alzheimer’s disease, alpha-synuclein in Parkinson’s disease, TDP-43 in ALS). AdS/Neuroscience methods are implicated in integrating multiscalar systems with different bulk-boundary space-time regimes (e.g. oncology tumors, fMRI + EEG imaging), entanglement (correlation) renormalization across scales (MERA, random tensor networks, melonic diagrams), entropy (possible system states), entanglement entropy (interrelated fluctuations and correlations across system tiers), and non-ergodicity (implied efficiency mechanisms since biology does not cycle through all possible configurations per temperature (thermotaxis), chemotaxis, and energy cues); Maxwell’s demon of biology (partition functions), conservation across system scales (biophysical gauge symmetry (system-wide conserved quantity)), and the presence of codes (DNA, codons, neural codes). A multiscalar AdS/CFT correspondence is mobilized in 4-tier ecosystem models (light-plankton-krill-whale and ion-synapse-neuron-network (AdS/Brain)).
Humanity’s constant project is expanding the range of attainable geography. Melville’s romance of the sea gives way to Kerouac’s romance of the road, and now the romance of space. In expanding into new geographies, markets (commerce) is the driving impulse, entailing a legal and judiciary system to order the new larger continuous marketplace, which brings a bigger overall scope of world under our control, and hence a new idea of who we are as subjects in this bigger domain.
Space Humanism is a concept of humanism based on the principles of inclusion, progress, and equity posited as a condition of possibility for a potential large-scale human movement into space. A philosophy of literature approach is used to contextualize Space Humanism, first through Melville-Foucault to articulate the mind-frame of extra-planetary geographies as one of human expansion, and second through posthuman philosophy extending from Shakespeare’s Renaissance humanism to contemporary enhancement-based theories of subjectivation.
Historical imaginaries outline subjectivation moments that have changed the whole notion who we are as humanity. Four examples are: the concept of the “new world” in Hegel’s philosophy, von Humboldt’s infographic maps, Baudelaire as the Painter of Modern Life, and Keats’s seeing the world in a new way upon reading an updated translation of Homer.
The reach to beyond-Earth geographies is a two-cultures project involving both arts and science. Technical competence is necessary to realize the aspirational, explorational, and survivalist aims of humanity pushing beyond planetary limits. Space was once a fantastic dream that is becoming quotidian with fourteen U.S. spaceports, six completed Blue Origin space tourist missions, and SpaceX having over 155 successful rocket launches including human space flights to and from the International Space Station. The notion of Space Human articulated through Shakespeare, Moby-Dick, and neuroenhancement informs the project of our reach to awaiting beyond-Earth geographies.
Quantum Information Science and Quantum Neuroscience.pptMelanie Swan
Mathematical advance in quantum information science is proceeding quickly and applies to many fields, particularly the complexities of neuroscience (here focusing on image-readable physical behaviors such as neural signaling, as opposed to higher-order operations of cognition, memory, and attention). Quantum mathematical models are extensible to neuroscience problem classes treating dynamical time series, diffusion, and renormalization in multiscalar systems. Approaches first reconstruct wavefunctions observed in EEG and fMRI scans. Second, single-neuron models (Hodgkin-Huxley, integrate-and-fire, theta neurons) and collective neuron models (neural field theories, Kuramoto oscillators) are employed to model empirical data. Third, genome physics is used to study time series sequence prediction in DNA, RNA, and proteins based on 3d+ complex geometry involving fields, curvature, knotting, and information compaction. Finally, quantum neuroscience physics is applied in AdS/Brain modeling, Chern-Simons biology (topological invariance), neuronal gauge theories, network neuroscience, and the chaotic dynamics of bifurcation and bistability (to explain epileptic and resting states). The potential benefit of this work is an improved understanding of disease and pathology resolution in humans.
Quantum information science enables a new tier of scientific problem-solving as exemplified in early-adopter fields, foundational tools in quantum cryptography, quantum machine learning, and quantum chemistry (molecular quantum mechanics), and advanced applications in quantum space science, quantum finance, and quantum biology
Grammatology and Performativity: A Critical Theory of Silence: Silence is a crucial device for subversion, opposition, and socio-political commentary, the theoretical underpinnings of which are just starting to be understood. This work illuminates another position in the growing field of critical silence studies, theorizing silence as an asset whose ontological value has been lost in a world of literal and figurative noise. Part 1 philosophizes silence as a continuation of Derrida’s grammatology project. Such a grammatology of silence valorizes silent thinking over noisy speaking, and identifies the deconstructive binary pairing not as silence-speaking, but rather as silence-noise. Noise has a simultaneous physical-virtual existence as Shannon entropy calculates signal-to-noise ratios in modern communications networks. Part 2 employs the philosophy of noise to assess what is conceptually necessary to overcome noise in a critical theory of silence. Malaspina draws from Simondon to argue that noise is a form of individuation, essentially a living thing with unstoppable growth potential, not defined by a binary on-off switch but as a matter of gradation. Hence different theory resources are required to oppose it. Part 3 then develops a critical theory of silence to oppose noise in both its physical and virtual instantiations, with the two arms of a deeply human positive performativity (Szendy, Bennett) and a beyond-computational posthumanism (Puar). The result is a novel critical theory of silence as positive performativity that destabilizes noise and recoups the ontological status of silence as not merely an empty post-modern reification but a meaningful actuality.
Philosophy-aided Physics at the Boundary of Quantum-Classical Reality The philosophical themes of truth-knowledge and appearance-reality are used to interrogate the contemporary situation of the quantum-classical boundary, and more broadly the quantum-classical-relativistic stratification of physical scale boundaries. The contemporary moment finds us at breakneck pace in the industrial information revolution, digitizing remaining matter-based industries into a seamless exchange between physical-digital reality. Digitized news is giving way to digitized money and perhaps in the farther future, digitized mindfiles (such as personalized connectome files for precision medicine, autologous (own-DNA) stem cell therapies, and CRISPR for Alzheimer’s disease prevention). Our technologies are allowing us control over vast new domains, the relativistic with GPS and space-faring, and the quantum with quantum computing, harnessing the properties of superposition, entanglement, and interference. Philosophy provides critical thinking tools that can help us understand and master these rapid shifts in science and technology to avoid an Adornian instrumental reality (subsuming humanity under societal structures) and to maintain a Heideggerian backgrounded and enabling relation with technology (versus technology enframing us into mindless standing reserve).
The philosophical theme underlying the investigation of the scales of planets, persons, and particles is the relationship between truth and knowledge (or appearance and reality). The truth-knowledge problem is whether knowledge of the truth, true knowledge, the reality under the appearance, is even possible. Three salient moments in the history of the truth-knowledge problem are examined here. These are the German idealism of Kant and Hegel, the deconstructive postmodernism of Foucault and Derrida, and the unclear leanings of the current moment. The German idealism lens incorporates the self-knowing subject as agent into the truth and knowledge problem. The postmodernist view breaks with the subject and emphasizes the hidden opposites in the formulations, the constant reinterpretation of meaning, and porous boundaries. The contemporary moment wonders whether truth-knowledge boundaries still hold, in a Benjaminian view of non-identity between truth and knowledge, and truth increasingly being seen as a Foucauldian biopolitical manufactured quantity. Contemporaneity has a bimodal distribution of the subject: the hyperself (the constantly digitally represented selfie self) and the alienated post-subject subject.
These moments in the truth and knowledge debate inflect into the scale considerations of relativity, classicality, and quantum mechanics. Whereas general relativity and quantum mechanics are domains of universality, totality, and multiplicity, everyday classical reality is squeezed in as a belt between the two multiplicities as the concretion of drawing a triangle or tossing a ball. Recasting truth and k
Comprehensive philosophical programs arise within a historical context (for Hegel and Derrida in the democracy-shaping moments of the French Revolution (1789) and the student-worker protests (1968) in which French politics serve as a global harbinger of contemporary themes). In the Derrida-Hegel relationship, there is more rapprochement concerning core notions of difference, history, and meaning-assignation than may have been realized. In particular, Hegel’s philosophy, despite being assumed to be a totalizing system, in fact indicates precisely some of the same kinds of revised metaphysics-of-presence formulations that Derrida exhorts, namely those that are flexible, expansive, and include non-identity and identity.
A crucial Derrida-Hegel interchange is that of différance and difference. Derrida develops the notion directly from Hegel (“Différance,” “The Pit and the Pyramid”), but only draws from the Encyclopedia, not Hegel’s masterwork, the Phenomenology of Spirit. For Derrida, the “A” in différance is inspired by the form of the pyramid in the capitalized letter and in Hegel’s comparing the sign “to the Egyptian Pyramid” (“Différance,” p. 3). Derrida invokes the symbolism of the pyramid, antiquity, and Egyptian hieroglyphics as an early semiotic system. However, when considering Hegel’s central definition of difference in the dialectical progression of thesis-antithesis-synthesis in the Phenomenology of Spirit (§§159-163), the articulations of différance and difference are remarkably aligned.
Parallel formulations are also seen in history as a series of reinterpretable events, and indexical wrappers as a mechanism for meaning assignation. The thinkers examine the universal and the particular by exploring regulative mechanisms such as law (natural and social). In Glas, Derrida highlights not the singular-universal relation, but the law of singularity and the law of universality relation as being relevant to Hegel’s Antigone interpretation (Glas, p. 142a), a theme continued in “Before the Law.” Finally (time permitting), there is a question whether the most valid critiques of Hegel (Nietzsche’s unreason and Benjamin’s non-synthesis), as alternatives to Hegelian dialectics, are visible in Derrida’s thought.
The upshot is that the two thinkers produce similar formulations, derived from different trajectories of philosophical work; a situation which points to the potential universality of fundamental solution classes to open-ended philosophical problems, including the future of democracy.
Quantum Moreness: Kantian Time and the Performative Economics of Multiplicity
There is no domain with greater moreness than that of the quantum. A philosophy-aided physics approach (postmodernism and Continental philosophy) examines the contemporary situation of quantum moreness (more time and space dimensions than are available classically). Quantum moreness is configured by quantum reality being probabilistic; a multiplicity of outcomes all co-existing in superposition until collapsed in measurement. The quantum mindset uses quantum moreness to solve problems by thinking in terms of the greater scalability afforded in time and space with the quantum properties of superposition, entanglement, and interference. Quantum studies fields proliferate in arts and sciences, raising the Levi-Straussian raw-cooked dilemma of how “traditional humanities” are to be named alongside “digital humanities” and “quantum humanities.” Kant facilitates the conceptualization of quantum moreness by insisting on the dual nature of time as transcendentally ideal and empirically real. Kant’s moreness is allness, the absolute totality and multiplicity of time at the ideal level. Each faculty (sensibility, understanding, reason) has its own species of the a priori synthetic unity of ideal time that precedes and conditions the operation of the faculty. Each faculty also has a concretized formulation of empirically-real time as the time series, which is the basis for the faculties to interoperate to perform the conception of any empirical object. Kant’s achievement of time interoperability has potential extensibility to other areas of temporal incompatibility such as the scales of general relativity, Newtonian mechanics (human-scale), and quantum mechanics. The quantum moreness mindset with which Kant connects the ideal-real is visible in the domain of economics, itself too an ideal-real construction. The quantum moreness of money configures the postmodern abstraction of global cryptocurrencies and smart contract pledges, the implicative hope of which is a post-debt capital world that restores the human esprit in the face of an increasingly intense technologized reality.
Blockchain Crypto Jamming: Subverting the Instrumental Economy
The ultimate subversion is money, refusing the pecuniary resources of the state. This project applies a philosophical and critical theory lens to examine the use of nomenclature in one of the most radical longitudinal transformations in contemporary times, the shift away from state-run monetary resources towards cryptocurrencies and smart contracts in citizen-determined decentralized financial networks.
A Cryptoeconomic Theory of Social Change is presented in which linguistic progression serves as a tracking mechanism. The steps to lasting change have their own vocabulary (Brandom). First, there is the social critique, the complaint about what is wrong, the negative side (Adorno and Horkheimer highlight instrumental reason and the empty culture industry). Second, there is the antidote, an alternative that can overcome the complaint, the positive side. Third, the solution becomes the new reality, and as a consequence, the whole of reality is now seen in this context, adopting its vocabulary (“fiat health” system for example, referring to the antiquated method). The social movement graduates from language game (Wittgenstein) to form of life (Jaeggi).
Blockchains are Occupy with teeth, notable in the level of personal responsibility-taking by individuals to steward their own financial resources. The crypto citizen is not merely trading CryptoKitties and Bored Ape Yacht Club tokens, but getting blocktime loans through DeFi liquidity pools instead of fiat banks, earning labor income in crypto, and shifting all economic activity to blockchain networks. The artworld signals mainstream acceptance with Christie’s non-fungible token digital artwork auctioned from Beeple for $61 million. At the global level, coin communities constitute a new form of Kardashev-level (planetary-scale) democracy. Blockchains emerge as a robust smart network automation technology for super-class projects ranging from space-faring to quantum computing and thought-tokening. The further stakes of this work are having a language-based theory of social change with broad applicability to social transformation.
This work argues that the emerging understanding of time in quantum information science can be articulated as a philosophical theory of change. Change and time are interrelated, and one can be used to interrogate the other, namely, a theory of change can be derived from a theory of time. What is new in quantum science is time being regarded as just another property to be engineered. At the quantum scale, time is reversible in certain ways, which is quite different from the everyday experience of time whose unidirectional arrow does not allow a dropped egg to reassemble. At the quantum scale of atoms, though, a particle retains the history of its trajectory, which may be retraced before collapsed in measurement.
Quantum scientists evolve systems backward and forward in time, controlling phase transitions with Floquet engineering. Quantum systems are entangled in time and space, with temporal correlations exhibiting greater multiplicity than spatial correlations. The chaotic time regimes of ballistic spread followed by saturation are implemented in quantum walks for faster search and heightened cryptosecurity. In quantum neuroscience, seizure may be explained by chaotic dynamics and normal resting state by Floquet-like periodic cycles. Time is revealed to have the same kinds of repeating structures as space (described by entanglement, symmetry, and topology), differently instantiated and controlled.
The quantum understanding of time can be propelled into a macroscale-theory of change through its connotation of a more flexible, malleable, probabilistic interface with reality. Change becomes less rigid. Probability is the lever of change, but notoriously difficult for humans to grasp, as we think better in storylines than statistics. The idea of manipulating quantum system properties in which time, space, dynamics (change), are all just parameters, is an empowering frame for the acceptance of change. The quantum mindset affords greater facility with probability-driven events (change).
Blockchains in Space: Non-Euclidean Spacetime and Tokenized Thinking - Two requirements for the large-scale beyond-terrestrial expansion of human intelligence into the universe are the ability to operate in diverse spatiotemporal regimes and to instantiate thinking in various formats. Newtonian mechanics describe everyday reality, but Einsteinian physics is needed for GPS and the orbital technologies of telescopes and spacecraft. Space agencies already integrate the Earth-day and the slightly-longer Martian-sol. A more substantial move into space requires facility with non-Euclidean spacetimes. One challenge is that general relativity and quantum mechanics are non-interoperable. However, the theories can be formulated together when considering black holes and quantum computing since geometric theories and gauge theories are both field-based. Quantum blockchains instantiate blockchain logic in quantum computational environments. Blockchains have their own temporal regime (blocktime: the number of blocks for an event to occur), and hence quantum blocktime is a non-classical functionality for operating in diverse spatiotemporal regimes. Thinking is a rule-based activity that is unrestricted by medium. Central to thinking is concepts, which are referenced by words. Word-types include universals, particulars, and indexicals which can be encoded into a formal system as thought-tokens, and registered to blockchains. Blockchains are contemplated as an automation technology for asteroid mining and space settlement construction, and thought-tokening adds an intelligence layer. Time and tokenized thinking come together in the idea of smart networks in space. In blockchain quantum smart networks, spatiotemporal regimes and thought-tokens are simply different value types (asset classes) coordinated with blockchain logic, towards the aim of extending human capabilities into the farther reaches of space.
Cryptography, entanglement, and quantum blocktime: Quantum computing offers a more scalable energy-efficient platform than classical computing and supercomputing, and corresponds more naturally to the three-dimensional structure of atomic reality. Blockchains are a decentralized digital economic system made possible by the 24-7 global nature of the internet.
Quantum Neuroscience: CRISPR for Alzheimer’s, Connectomes & Quantum BCIsMelanie Swan
This talk provides an introduction to quantum computing and how it may be deployed to study the human brain and its diseases of pathology and aging. Refined to its present state over centuries, the brain is one of the most complex systems known, with 86 billion neurons and 242 trillion synapses connected in intricate patterns and rewired by synaptic plasticity. Research continues to illuminate the mysteries of the brain. Quantum computing provides a more capacious architecture with greater scalability and energy efficiency than current methods of classical computing and supercomputing, and more naturally corresponds to the three-dimensional structure of atomic reality. The vision for quantum neuroscience is to model the nature of the brain exactly as it is, in three-dimensional atomically-accurate representations. Neuroscience (particularly genetic disease modeling, connectomics, and synaptomics) could be the “killer application” of quantum computing. Implementations in other industries are also important, including in quantum finance, quantum cryptography using Shor’s factoring algorithm (“the Y2K of Crypto”), Grover’s search, quantum chemistry, eigensolvers, quantum machine learning, and continuous-time quantum walks. Quantum computing is a high-profile worldwide scientific endeavor with platforms currently available via cloud services (IBM Q 27-qubit, IonQ 32-qubit, Rigetti 19Q Acorn) and is in the process of being applied in various industries including computational neuroscience.
Art Theory: Two Cultures Synthesis of Art and ScienceMelanie Swan
Thesis: Aesthetic resources contribute broadly to the human endeavor of progress, self-understanding, and science, beyond the immediate experience of art. Aesthetic Resources are frameworks, concepts, and modes of expression in art, literature, and philosophy that capture the imagination and the intellect through the senses. The role of art is to inspire the future: the romance of the sea, the open road, space.
The arts are a hallmark of civilization, but can their benefit be crystallized as aesthetic resources that can be mobilized to new situations? How can aesthetic resources help in moments of crisis?
A worldwide social identity crisis has been provoked by pandemic recovery, politics, equity, and environmental sustainability. Philosophical and aesthetic resources can help. Understanding art as a reflection of who we are as individuals and groups, this talk explores conceptualizations of art, with examples, in different periodizations from the 1800s to the present. A marquis definition as to what constitutes an artwork is Adorno’s, for whom the work must promulgate its own natural law and engage in novel materials manipulation. For many theorists, art is the pressing of our self-concept into concrete materiality (whether pyramids, sculpture, or painting). What do contemporary periodizations of art mean to our current and forward-looking self-concept? Recent eras include the neo-avant-gardes of 1945, the conceptual art of the 1960s, and post-conceptual art starting in the 1970s, produced generatively with found materials, the digital domain, and audience interactivity. What is the now-current idea of art? Is today’s Baudelairian flâneur and Balzacian modern hero incarnated in the quantum aesthetic imaginary and the digital cryptocitizen? Far from an “end of art” thesis sometimes attributed to Hegel, aesthetic practices are more relevant than ever. Individually and societally, we are reinventing creative energy and productive imagination in venues from science, technology, health, and biology to the arts.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
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After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
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👨🏫 Andras Palfi, Senior Product Manager, UiPath
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Assuring Contact Center Experiences for Your Customers With ThousandEyes
Big Data and the Quantified Self
1. Big Data and the
Quantified Self
October 28, 2013
National Consumer Res Ctr, Helsinki, Finland
Slides: http://slideshare.net/LaBlogga
Melanie Swan
MS Futures Group
+1-650-681-9482
@LaBlogga, @DIYgenomics
www.MelanieSwan.com
m@melanieswan.com
http://www.youtube.com/TechnologyPhilosophe
2. About Melanie Swan
Founder DIYgenomics, science and
technology innovator and philosopher
Singularity University Instructor, IEET
Affiliate Scholar, EDGE Contributor
Education: MBA Finance, Wharton; BA
French/Economics, Georgetown Univ
Work experience: Fidelity, JP Morgan, iPass,
RHK/Ovum, Arthur Andersen
Sample publications:
Swan, M. Crowdsourced Health Research Studies: An Important Emerging Complement to Clinical Trials in the Public
Health Research Ecosystem. J Med Internet Res 2012, Mar;14(2):e46.
Swan, M. Scaling crowdsourced health studies: the emergence of a new form of contract research organization.
Personalized Medicine 2012, Mar;9(2):223-234.
Swan, M. Steady advance of stem cell therapies. Rejuvenation Res 2011, Dec;14(6):699-704.
Swan, M., Hathaway, K., Hogg, C., McCauley, R., Vollrath, A. Citizen science genomics as a model for crowdsourced
preventive medicine research. J Participat Med 2010, Dec 23; 2:e20.
Swan, M. Multigenic Condition Risk Assessment in Direct-to-Consumer Genomic Services. Genet Med 2010,
May;12(5):279-88.
Swan, M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized
medicine and quantified self-tracking. Int J Environ Res Public Health 2009, 2, 492-525.
October 28, 2013
QS Big Data
Source: http://melanieswan.com/publications.htm
2
3. Conceptualizing Big Data Categories
Personal Data
Tension: Individual vs Institution
Group Data
Sense of data belonging to a group
October 28, 2013
QS Big Data
3
5. What is the Quantified Self?
Individual engaged in the selftracking of any kind of biological,
physical, behavioral, or
environmental information
Data acquisition through
technology: wearable sensors,
mobile apps, software interfaces,
and online communities
Proactive stance: obtain and act
on information
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June
2013, 1(2): 85-99.
5
6. QS Sensor Mania! Wearable Electronics
Smartphone, Fitbit, Smartwatch (Pebble), Electronic T-shirt (Carre)
Smartring (ElectricFoxy), Electronic tattoos (mc10), $1 blood API
(Sano Intelligence), Continuous Monitors (Medtronic)
October 28, 2013
QS Big Data
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified
Self 2.0. J Sens Actuator Netw 2012.
6
7. Wearable Personal Information Ecosystem
Smart Gadgetry Creates Continuous Personal Information Climate
New Wearable Categories:
Smartwatch and AR/Glass
Smartphone
PC/Tablet/Cloud
October 28, 2013
QS Big Data
AR = Augmented Reality
7
8. Next-gen Mini: BioSensor Electronic Tattoos
Wearable Electronics: Detect External BioChemical
Threats and Track Internal Vital Signs
Electrochemical Sensors
Chemical Sensors
October 28, 2013
QS Big Data
Disposable Electronics
Source: http://www.jacobsschool.ucsd.edu/pulse/winter2013/page3.shtml#tattoos
Tactile Intelligence:
Haptic Data Glove
8
9. Quantified Self Worldwide Community
Goal: personalized knowledge through
quantified self-tracking
‘Show n tell’ meetups
What did you do? How did you do it? What
did you learn?
Videos, Conferences, Meetup Groups
October 28, 2013
QS Big Data
Source: Swan, M. Overview of Crowdsourced Health Research Studies. 2012.
9
10. October 28, 2013
QS Big Data
Source: http://www.meetup.com/Quantified-Self-Biohacking-Finland/
10
11. Quantified Self Project Examples
Food consumption (1 yr)1 and the Butter Mind study2
Study
Low-cost home-administered blood, urine, saliva tests
Cholestech LDX
home cholesterol test
October 28, 2013
QS Big Data
1
2
OrSense continuous non-invasive
glucose monitoring
Source: http://flowingdata.com/2011/06/29/a-year-of-food-consumption-visualized
Source: http://quantifiedself.com/2011/01/results-of-the-buttermind-experiment
ZRT Labs dried
blood spot tests
11
12. Quantified Self Measurements…
Physical Activities
Diet and Nutrition
Location, architecture, weather, noise, pollution, clutter, light, season
Situational Variables
Mood, happiness, irritation, emotion, anxiety, esteem, depression, confidence
IQ, alertness, focus, selective/sustained/divided attention, reaction, memory,
verbal fluency, patience, creativity, reasoning, psychomotor vigilance
Environmental Variables
Calories consumed, carbs, fat, protein, specific ingredients, glycemic index,
satiety, portions, supplement doses, tastiness, cost, location
Psychological, Mental, and Cognitive States and Traits
Miles, steps, calories, repetitions, sets, METs1
Context, situation, gratification of situation, time of day, day of week
Social Variables
October 28, 2013
QS Big Data
Influence, trust, charisma, karma, current role/status in the group or social network
METs = Metabolic equivalents Source: http://measuredme.com/2012/10/building-thatperfect-quantified-self-app-notes-to-developers-and-qs-community-html/
1
12
13. The Quantified Self is Mainstream
Self-tracking statistics
60% US adults track weight, diet, or exercise
33% US adults monitor blood sugar, blood pressure,
headaches, or sleep patterns
9% receive text message health alerts
40,000 smartphone health applications
QS thought leadership
Press : BBC, Forbes, and Vanity Fair
Electronics show focus at CES 2013
Health 2.0: “500+ companies making
self-management tools; VC funding up 20%”
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data
June 2013, 1(2): 85-99.
13
14. Hype Curves per Google Trends
2011
October 28, 2013
QS Big Data
2013
2011
2013
14
15. QS Experimentation Motivation and Features
DIYgenomics QS Study (n=37)
Desired outcome: optimality and
improvement (vs pathology resolution)
Personalized intervention for depression,
low energy, sleep quality, productivity, and
cognitive alertness
Rapid experimental iteration through
solutions and kinds of solutions
Resolution point found within weeks
Pragmatic problem-solving focus, little
introspection
October 28, 2013
QS Big Data
Source: DIYgenomics Knowledge Generation through Self-Experimentation Study
http://genomera.com/studies/knowledge-generation-through-self-experimentation
15
16. History of the Quantified Self
Sanctorius of Padua 16th c: energy
expenditure in living systems; 30
years of QS weight/food data
QS Philosophers
Epicureans, Heidegger, Foucault): ‘care
of the self’
‘Self’: recent concept of modernity
QS: contemporary formalization using
measurement, science, and
technology to bring order and control
to the natural world, including the
human body
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June
2013, 1(2): 85-99.
16
17. Sensor Mania!
October 28, 2013
QS Big Data
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the
Quantified Self 2.0. J Sens Actuator Netw 2012.
17
18. Wireless Internet-of-Things (IOT)
Image credit: Cisco
October 28, 2013
QS Big Data
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0.
J Sens Actuator Netw 2012.
18
19. 6 bn Current IOT devices to double by 2016
October 28, 2013
QS Big Data
Source: http://www.businessinsider.com/growth-in-the-internet-of-things-2013-10?IR=T
19
20. IOT World of Smart Matter
IOT Definition: digital networks of
physical objects linked by the Internet
that interact through web services
Usual gadgetry (e.g.; smartphones,
tablets) and now everyday objects:
cars, food, clothing, appliances,
materials, parts, buildings, roads
Embedded microprocessors in 5%
human-constructed objects (2012)1
October 28, 2013
QS Big Data
Source: Vinge, V. Who’s Afraid of First Movers? The Singularity Summit 2012.
http://singularitysummit.com/schedule
1
20
21. IOT Contributing to Explosion of Big Data
Big Data: data sets too large and
complex to process with on-hand
database management tools
Examples
Walmart : 1 million transactions/hr
transmitted to 3 PB database
BBC: 7 PB video served/month from
100 PB physical disk space
Structured and unstructured data
(not pre-defined)
October 28, 2013
QS Big Data
Source: http://en.wikipedia.org/wiki/Big_data, http://wikibon.org/blog/big-data-statistics
21
22. Defining Trend of Current Era: Big Data
Annual data creation on the order of zetabytes
90% of the world’s data created in the last 2 years
Fastest growing segment: human biology-related data
2 year doubling cycle
October 28, 2013
QS Big Data
Source: Mary Meeker, Internet Trends, http://www.kpcb.com/insights/2013-internet-trends
http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/healthcare-leveraging-big-data-paper.pdf
22
23. QS is inherently a Big Data problem
Data collection, processing, analysis
Cloud computing for consumer processing
Local computing tools are not available to store,
query, and manipulate QS data sets
Cloud-based analysis: Predictive modeling,
natural-language processing, machine learning
algorithms over very-large data sets of
heterogeneous data
Rapid growth in QS data sets
Manually-tracked ‘small data’ is now
automatically-collected ‘big data’
Examples: heart rate monitor data - 250
samples/second (9 GB/person/month);
personal health ‘omics’ files
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data
June 2013, 1(2): 85-99.
23
24. QS Big Data: Personal Health ‘Omics’
DNA:
SNP mutations
DNA: Structural
variation
RNA expression
profiling
Health 2.0:
Personal Health
Informatics
Proteomics
Microbiomics
Epigenetics
Metabolomics
October 28, 2013
QS Big Data
Source: Academic papers re: integrated health data streams: Auffray C, et al. Looking back at genomic medicine in 2011. Genome Med. 2012
Jan 30;4(1):9. Chen R et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012 Mar 16;148(6):1293-307.
24
25. Big Data: Integrated QS Data Streams
Omics Data Streams
Genome
SNP mutations
Structural variation
Epigenetics
Microbiome
Traditional Data Streams
Personal and Family
Health History
Proteome
Self-reported data:
health, exercise,
food, mood
journals, etc.
Prescription History
Transcriptome
Metabolome
Quantified Self Data
Streams
Mobile App Data
Lab Tests: History
and Current
Demographic Data
Quantified Self
Device Data
Standardized
Instrument Response
Biosensor Data
Objective Metrics
Diseasome
Environmentome
October 28, 2013
QS Big Data
Swan, M. Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory
Biocitizen. J Pers Med 2012, 2(3), 93-118.
Legend: Consumer-available
25
26. APIs and Multi-QS Data Stream Integration
October 28, 2013
QS Big Data
26
27. Fluxstream Unified QS Dashboard
October 28, 2013
QS Big Data
Source: http://johnfass.wordpress.com/2012/09/06/bodytrackfluxtream/
27
28. Sen.se Integrated QS Dashboard
‘Mulitviz’ display: investigate correlation between coffee
consumption, social interaction, and mood
October 28, 2013
QS Big Data
Source: http://blog.sen.se/post/19174708614/mashups-turning-your-data-intosomething-useable-and
28
29. Wholly different concept and relation to data
Formerly everything signal, now 99% noise
Medium of big data opens up new methods:
Exception, characterization, variability, pattern recognition,
correlation, prediction, early warnings
Allows attitudinal shift to active from reactive
Two-way communication: translate biometric variability in the
personal informatics climate to real-time recommendations
Example: degradation in sleep quality and hemoglobin A1C levels
predict diabetes onset by 10 years1
October 28, 2013
QS Big Data
Source: Heianza et al. High normal HbA(1c) levels were associated with
impaired insulin secretion. Diabet Med 2012. 29:1285-1290.
1
29
30. Big Data opens up new Methods
Google: large corpora and simple algorithms
Foundational characterization (previously unavailable)
Longitudinal baseline measures of internal and external daily
rhythms, normal deviation patterns, contingency adjustments,
anomaly, and emergent phenomena
New kinds of Pattern Recognition (different structures)
Analyze data in multiple paradigms: time, frequency, episode, cycle,
and systemic variables
New trends, cyclicality, episodic triggers, and other elements that
are not clear in traditional time-linear data
Multi-disciplinarity
Turbulence, topology, chaos, complexity, etc. models
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data
June 2013, 1(2): 85-99.
30
31. Opportunity: QS Data Commons
Common repository for personal informatics
data streams
Fitbit, Jawbone UP, Nike, Withings, myZeo,
23andMe, Glass, Pebble, Basis, BodyMedia
Architecting consumer-friendly models
Open-access databases, developer APIs, frontend web services and mobile apps
(Precedent: public genotype/phenotype data)
Accommodate multi-tier privacy standards
Ecosystem value propositions: service providers,
research community, biometric data-owners
Role of public and private service providers
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data
June 2013, 1(2): 85-99.
31
32. Github: de facto
QS Data
Commons
October 28, 2013
QS Big Data
Source: https://github.com/beaugunderson/genome
32
33. QS Frontier: Mental Performance Optimization
Mood Management Apps from
Mobilyze and M. Morris
PTSD App
‘Siri 2.0’ Personal Virtual Coach
from DIYgenomics
Source:
http://www.ptsd.va.gov/pu
blic/pages/ptsdcoach.asp
Sources: http://cbits.northwestern.edu and
http://quantifiedself.com/2009/03/a-few-weeks-ago-i
October 28, 2013
QS Big Data
Source: DIYgenomics Social Intelligence Study
http://diygenomics.pbworks.com/w/page/48946791/social_intelligence
33
34. Next-gen QS Services: Quality of Life
QS Aspiration Apps:
Happiness, Emotive
State (personal and
group), Well-being,
Goal Achievement
October 28, 2013
QS Big Data
Category and Name
Website URL
Happiness Tracking
Track Your Happiness
http://www.trackyourhappiness.org/
Mappiness
http://www.mappiness.org.uk/
The H(app)athon Project
http://www.happathon.com/
MoodPanda
http://moodpanda.com/
TechurSelf
http://www.techurself.com/urwell
Emotion Tracking and Sharing
Gotta Feeling
http://gottafeeling.com/
Emotish
http://emotish.com/
Feelytics
http://feelytics.me/
Expereal
http://expereal.com/
Population-level Emotion Barometers
We Feel Fine
http://wefeelfine.org/
moodmap
http://themoodmap.co.uk/
Pulse of the Nation
http://www.ccs.neu.edu/home/amislove/twittermood/
Twitter Mood Map
http://www.newscientist.com/blogs/onepercent/2011/09/twitt
er-reveals-the-worlds-emo-1.html
Wisdom 2.0
http://wisdom2summit.com/
Personal Wellbeing Platforms
GravityEight
http://www.gravityeight.com/
MindBloom
https://www.mindbloom.com/
Get Some Headspace
http://www.getsomeheadspace.com/
Curious
http://wearecurio.us/
uGooder
http://www.ugooder.com/
Goal Achievement Platforms
uMotif
http://www.uMotif.com/
DidThis
http://blog.didthis.com/
Schemer
https://www.schemer.com/ (personalized recommendations)
Pledge/Incentive-Based Goal Achievement Platforms
GymPact
http://www.gym-pact.com/
Stick
http://www.stickk.com/
Beeminder
https://www.beeminder.com/
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June
2013, 1(2): 85-99.
34
35. Next-gen QS Services: Behavior Change
October 28, 2013
QS Big Data
Source: http://askmeevery.com/
35
36. Next-gen QS Services: Behavior Change
Shikake: Sensors embedded
in physical objects to trigger
a physical or psychological
behavior change
Examples:
Transparent trash cans
Trash cans playing an
appreciative sound to
encourage litter to be deposited
Stairs light up on approach
Appreciative ping/noise from
QS gadgetry
October 28, 2013
QS Big Data
Source: http://mtmr.jp/en/papers/taai2013v2.pdf
36
37. Next-gen QS Services: 3D Quantification
BodyMetrics and Poikos:
Fitness and Clothing
Customization Apps
OMsignal: Smart Apparel
24/7 Biometric Monitoring
October 28, 2013
QS Big Data
37
38. Continuous Information Climate
Fourth-person perspective: Immersed in infinite data
flow, we shed bits of information to the data flow, the
data flow responds by sending information to us
October 28, 2013
QS Big Data
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June
2013, 1(2): 85-99.
38
39. Building Exosenses for the Qualified Self
Extending our senses in new ways to perceive data as sensation
Magnetic Sense: Finger and Arm Magnets
North Paw Haptic Compass Anklet and Heart Spark
http://www.youtube.com/watch?v=D4shfNufqSg
http://sensebridge.net/projects/heart-spark
October 28, 2013
QS Big Data
Serendipitous Joy: Smiletriggered EMG muscle sensor
with an LED headband display
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified
Self 2.0. J Sens Actuator Netw 2012.
39
40. Exosenses as Quantified Intermediates
Networked quantified intermediates for
human senses: smarter, visible, sharable
through big data processing
Vague sense of heart rate variability, blood
pressure; haptically-available exosenses
make the data explicit
Haptics, audio, visual, taste, olfactory
mechanisms to make metrics explicit: heart
rate variability, blood pressure, galvanic skin
response, stress level
Skill as exosense: technology as memory,
self-experimentation as a form of exosense
October 28, 2013
QS Big Data
Source: web.mit.edu/newsoffice/2012/human-body-on-a-chip-research-funding-0724.html
Nose-on-a-chip
Gut-on-a-chip
Lung-on-a-chip
40
41. Neural Tracking: QS Big Data Frontier
24/7 Consumer EEG, Eye-tracking, Emotion-Mapping, Augmented Reality Glasses
Consumer EEG Rigs
Augmented Reality Glasses
1.0
2.0
October 28, 2013
QS Big Data
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens
Actuator Netw 2012.
41
42. QS Big Data: Biocitizen Volition
1. Continuous health information climate
Automated digital health monitoring, self-tracking devices,
and mobile apps providing personalized recommendations
2. Peer collaboration and
health advisors
Individual
Health social networks, crowdsourced
studies, health advisors, wellness
coaches, preventive care plans,
boutique physicians, genetics coaches,
aestheticians, medical tourism
3. Public health system
Deep expertise of traditional health system
for disease and trauma treatment
October 28, 2013
QS Big Data
Source: Extended from Swan, M. Emerging patient-driven health care models: an examination of health social networks, consumer
personalized medicine and quantified self-tracking. Int. J. Environ. Res. Public Health 2009, 2, 492-525.
42
45. Group Data: Smart City, Future City
October 28, 2013
QS Big Data
Image: http://www.sydmead.com
45
46. Global Population: Growing and Aging
October 28, 2013
QS Big Data
Source: UN Habitat – 2010
http://avondaleassetmanagement.blogspot.com/2012/05/japan-aging-population.html
46
47. 3 billion new Internet users by 2020
October 28, 2013
QS Big Data
Source: Peter Diamandis Singularity University
47
48. Human Urbanization: Living in Cities
Over 50% worldwide population in 2008
5 billion in 2030 (estimated)
Megacity: (>10 million and possibly 2,000/km 2)
October 28, 2013
QS Big Data
48
50. Big Urban Data: Killer Apps
Adaptive lighting, smart waste, pest control, hygiene
management, eTolls, public transportation, traffic management,
smart grid, asset tracking, parking
Flexible services responding in real-time to individual and
community-level demand
October 28, 2013
QS Big Data
Source: MIT Senseable City Lab
50
51. Data Signature of Humanity
MIT SENSEable City Lab – the Real-Time City
October 28, 2013
QS Big Data
Source: http://senseable.mit.edu/signature-of-humanity/
51
52. 3D Buildings + Population Density
October 28, 2013
QS Big Data
Source: ViziCities
52
53. 3D Tweet Landscape
October 28, 2013
QS Big Data
Source: http://vimeo.com/67872925
http://www.slideshare.net/robhawkes/bringing-cities-to-life-using-big-data-webgl
53
54. 3D Urban Data Viz: Decision-making Tool
October 28, 2013
QS Big Data
Source: http://www.wired.com/autopia/2013/08/london-underground-3d-map/
54
55. Group Data: Office Building Community
October 28, 2013
QS Big Data
Source: http://www.siembieda.com/burg.html, BURG, San Jose CA 2010
55
56. Big Data 3D Printed Dwellings of the Future
Living Treehouses – Mitchell Joachim
Masdar, Abu Dhabi – Energy City of the Future
October 28, 2013
QS Big Data
Himalayas Water Tower
57. Urban Agriculture: Vertical Farms
San Diego, California
(planned)
October 28, 2013
QS Big Data
Singapore (existing)
57
59. Transportation Revolution
Solar Power: Tesla + Solar City
Personalized Pod Transport
October 28, 2013
QS Big Data
Self-Driving Car
Source: Google's Self-Driving Cars Complete 300K Miles Without Accident, Deemed Ready for Commuting
http://techcrunch.com/2012/08/07/google-cars-300000-miles-without-accident/
59
60. Crowdsourcing
October 28, 2013
QS Big Data
Source: Eric Whitacre's Virtual Choir 3, 'Water Night' (2012), http://www.youtube.com/watch?v=V3rRaL-Czxw
60
61. Pervasiveness of Crowd Models
Crowdsourcing: coordination of large numbers of
individuals (the crowd) through an open call on the
Internet in the conduct of some sort of activity
Economics: crowdsourced labor marketplaces, crowdfunding,
grouppurchasing, data competition (Kaggle)
Politics: flashmobs, organizing, opinion-shifting, data-mining
Social: blogs, social networks, meetup, online dating
Art & Entertainment: virtual reality, multiplayer games
Education: MOOCs (massively open online courses)
Health: health social networks, digital health experimentation
communities, quantified self
Digital public goods: Wikipedia, online health databanks, data
commons resources, crowdscience competitions
October 28, 2013
QS Big Data
61
62. Genomera – Crowdsourced Study Platform
October 28, 2013
QS Big Data
Source: http://genomera.com/studies/dopamine-genes-and-rapid-realityadaptation-in-thinking
62
64. But wait…Limitations and Risks
Transition to access not ownership models
Data rights and responsibilities
Regulatory and policy tensions
Personal data and group data
Surveillance (top-down) vs souveillance (bottom-up)
Multi-tier privacy and sharing preferences
Digital divide accessibility, non-discrimination
Precedent = Uninformed Consumer: Lack of access
conferred (e.g.; health data, genomics, credit scoring)
Consumer non-adoption, ease-of-use, social
acceptance, meaningful value propositions
October 28, 2013
QS Big Data
64
65. Proliferation of New QS Big Data Flows
QS Device Data
Personal IOT Data
Cell phone, wearable electronics data
Smartphone digital identity & payment
Personal Urban Data
Biometric data (HRM), personal genomic data
Personal medical and health data
QS neural-tracking eye-tracking affect data
Smart home, smart car
Smart city data (e.g.; transportation)
Personal Robotics Data
October 28, 2013
QS Big Data
65
66. Top 10 QS Big Data Trends
Personal Data
Group Data
QS Device Ecosystem
Internet-of-Things (IOT)
Sensor Networks
3D Information
Visualization
Wearable Electronics
Smart City
Future City
Megacity
Growth
Urban Data
October 28, 2013
QS Big Data
Biocitizen
Self-Empowerment
DIY Attitude
Crowdsourcing
3 billion New
People Online
66
67. Heidegger and Big Data
Technology is not good or bad in
itself, technology is an enabler, not a
means to an end (Kant: end not
means)
Our attunement to the background
of technology as a capacity for
revealing the world could help us
away from our lostness in daily
projects to see the possibilities for
the true meaningfulness of our being
October 28, 2013
QS Big Data
Source: Heidegger, M. The Question Concerning Technology, 1954
67
68. QS Big Data Summary
Next-gen QS services
IOT continuous personal information climates
QS Big Data
Wholly different relation to data: 99% noise
Rights and responsibilities model of data access
Group Data
Wearable Electronics as the QS platform
Improve quality of life, facilitate behavior change
Megacity growth, urban data flow, 3 bn coming online
Personal Data
Technology-enabled biocitizen-consumer takes action
October 28, 2013
QS Big Data
68
69. Big Data and the
Quantified Self
kittos!
Questions?
October 28, 2013
National Consumer Res Ctr, Helsinki, Finland
Slides: http://slideshare.net/LaBlogga
Melanie Swan
MS Futures Group
+1-650-681-9482
@LaBlogga, @DIYgenomics
www.MelanieSwan.com
m@melanieswan.com
http://www.youtube.com/TechnologyPhilosophe